Csaw: A Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows

Nucleic Acids Research

Volumn 44, Issue 5, 2015, Pages e45-

  Lun, Aaron T L ^a,b   Smyth, Gordon K ^a,b  

^a WALTER AND ELIZA HALL INSTITUTE OF MEDICAL RESEARCH   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

HISTONE; TRANSCRIPTION FACTOR; PROTEIN BINDING;

ANIMAL EXPERIMENT; ARTICLE; BIOINFORMATICS; CHROMATIN IMMUNOPRECIPITATION; COMPUTER PROGRAM; CONTROLLED STUDY; DATA ANALYSIS; DATA BASE; EMBRYONIC STEM CELL; GENOME; HISTONE METHYLATION; MOUSE; NONHUMAN; PRIORITY JOURNAL; PROTEIN BINDING; SEQUENCE ANALYSIS; SIMULATION; ANIMAL; BINDING SITE; CYTOLOGY; GENETICS; GENOMICS; HIGH THROUGHPUT SEQUENCING; INFORMATION PROCESSING; METABOLISM; MOUSE EMBRYONIC STEM CELL; PROCEDURES; SOFTWARE;

ANIMALS; BINDING SITES; CHROMATIN IMMUNOPRECIPITATION; DATASETS AS TOPIC; GENOME; GENOMICS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HISTONES; MICE; MOUSE EMBRYONIC STEM CELLS; PROTEIN BINDING; SOFTWARE; TRANSCRIPTION FACTORS;

EID: 84963820955     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkv1191     Document Type: Article

References (32)

1. Zhang, Y., Liu, T., Meyer, C.A., Eeckhoute, J., Johnson, D.S., Bernstein, B.E., Nusbaum, C., Myers, R.M., Brown, M., Li, W., et al. (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol., 9, R137.
2. Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y.C., Laslo, P., Cheng, J.X., Murre, C., Singh, H. and Glass, C.K. (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell, 38, 576-589.
3. Zang, C., Schones, D.E., Zeng, C., Cui, K., Zhao, K. and Peng, W. (2009) A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics, 25, 1952-1958.
4. Ross-Innes, C.S., Stark, R., Teschendorff, A.E., Holmes, K.A., Ali, H.R., Dunning, M.J., Brown, G.D., Gojis, O., Ellis, I.O., Green, A.R., et al. (2012) Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature, 481, 389-393.
5. Pal, B., Bouras, T., Shi, W., Vaillant, F., Sheridan, J.M., Fu, N., Breslin, K., Jiang, K., Ritchie, M.E., Young, M., et al. (2013) Global changes in the mammary epigenome are induced by hormonal cues and coordinated by Ezh2. Cell Rep., 3, 411-426.
6. Mahony, S., Edwards, M.D., Mazzoni, E.O., Sherwood, R.I., Kakumanu, A., Morrison, C.A., Wichterle, H. and Gifford, D.K. (2014) An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding. PLoS Comput. Biol., 10, e1003501.
7. Heinig, M., Colomé-Tatché3, M., Taudt, A., Rintisch, C., Schafer, S., Pravenec, M., Hubner, N., Vingron, M. and Johannes, F. (2015) histoneHMM: differential analysis of histone modifications with broad genomic footprints. BMC Bioinformatics, 16, 60.
8. McCarthy, D.J., Chen, Y. and Smyth, G.K. (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res., 40, 4288-4297.
9. Lund, S.P., Nettleton, D., McCarthy, D.J. and Smyth, G.K. (2012) Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Stat. Appl. Genet. Mol. Biol., 11, doi:10.1515/1544-6115.1826.
10. Robinson, M., McCarthy, D. and Smyth, G. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140.
11. Liang, K. and Keleş, S. (2012) Detecting differential binding of transcription factors with ChIP-seq. Bioinformatics, 28, 121-122.
12. Lun, A.T.L. and Smyth, G.K. (2014) De novo detection of differentially bound regions for ChIP-seq data using peaks and windows: controlling error rates correctly. Nucleic Acids Res., 42, e95.
13. Nix, D.A., Courdy, S.J. and Boucher, K.M. (2008) Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks. BMC Bioinformatics, 9, 523.
14. Shen, L., Shao, N.Y., Liu, X., Maze, I., Feng, J. and Nestler, E.J. (2013) diffReps: detecting differential chromatin modification sites from ChIP-seq data with biological replicates. PLoS One, 8, e65598.
15. Zhang, Y., Lin, Y.H., Johnson, T.D., Rozek, L.S. and Sartor, M.A. (2014) PePr: a peak-calling prioritization pipeline to identify consistent or differential peaks from replicated ChIP-Seq data. Bioinformatics, 30, 2568-2575.
16. Huber, W., Carey, V.J., Gentleman, R., Anders, S., Carlson, M., Carvalho, B.S., Bravo, H.C., Davis, S., Gatto, L., Girke, T., et al. (2015) Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods, 12, 115-121.
17. Obenchain, V. (2015) Overview of the csaw package. Bioconductor Newsletter, https://www.bioconductor.org/help/newsletters/2015 January/.
18. Lun, A.T.L., Chen, Y. and Smyth, G.K. (2016) It's DE-licious: a recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR. Methods Mol. Biol., In press.
19. Clouaire, T., Webb, S. and Bird, A. (2014) Cfp1 is required for gene expression dependent H3K4me3 and H3K9 acetylation in embryonic stem cells. Genome Biol., 15, 451.
20. Liao, Y., Smyth, G.K. and Shi, W. (2013) The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res., 41, e108.
21. Robertson, G., Hirst, M., Bainbridge, M., Bilenky, M., Zhao, Y., Zeng, T., Euskirchen, G., Bernier, B., Varhol, R., Delaney, A., et al. (2007) Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods, 4, 651-657.
22. Kharchenko, P.V., Tolstorukov, M.Y. and Park, P.J. (2008) Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat. Biotechnol., 26, 1351-1359.
23. Robinson, M.D. and Oshlack, A. (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol., 11, R25.
24. Bolstad, B.M., Irizarry, R.A., Astrand, M. and Speed, T.P. (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19, 185-193.
25. Ballman, K.V., Grill, D.E., Oberg, A.L. and Therneau, T.M. (2004) Faster cyclic loess: normalizing RNA arrays via linear models. Bioinformatics, 20, 2778-2786.
26. Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W. and Smyth, G.K. (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 43, e47.
27. Galvis, L.A., Holik, A.Z., Short, K.M., Pasquet, J., Lun, A.T., Blewitt, M.E., Smyth, I.M., Ritchie, M.E. and Asselin-Labat, M.L. (2015) Repression of Igf1 expression by Ezh2 prevents basal cell differentiation in the developing lung. Development, 142, 1458-1469.
28. Holik, A.Z., Galvis, L.A., Lun, A.T., Ritchie, M.E. and Asselin-Labat, M.L. (2015) Transcriptome and H3K27 tri-methylation profiling of Ezh2-deficient lung epithelium. Genomics Data, 5, 346-351.
29. Chen, L., Wang, C., Qin, Z.S. and Wu, H. (2015) A novel statistical method for quantitative comparison of multiple ChIP-seq datasets. Bioinformatics, 31, 1889-1896.
30. Manna, S., Kim, J.K., Bauge, C., Cam, M., Zhao, Y., Shetty, J., Vacchio, M.S., Castro, E., Tran, B., Tessarollo, L., et al. (2015) Histone H3 Lysine 27 demethylases Jmjd3 and Utx are required for T-cell differentiation. Nat. Commun., 6, 8152.
31. Frazee, A.C., Sabunciyan, S., Hansen, K.D., Irizarry, R.A. and Leek, J.T. (2014) Differential expression analysis of RNA-seq data at single-base resolution. Biostatistics, 15, 413-426.
32. Lun, A.T.L. and Smyth, G.K. (2015) From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data [version 1; referees: 1 approved]. F1000Research, 4, 1080.